DeepLabV3+Ensemble for Diagnosis of Cardiac Transplant Rejection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F23%3A00084646" target="_blank" >RIV/00023001:_____/23:00084646 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-44137-0_10" target="_blank" >http://dx.doi.org/10.1007/978-3-031-44137-0_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-44137-0_10" target="_blank" >10.1007/978-3-031-44137-0_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DeepLabV3+Ensemble for Diagnosis of Cardiac Transplant Rejection

Popis výsledku v původním jazyce

Heart transplantation is a complex procedure, often joined with complications such as cardiac transplant rejection. Current diagnostic methods include regular invasive and time-consuming biopsies followed by histopathological analysis. Deep learning has the potential to significantly enhance speed and objectivity and introduce new information from the obtained sample to increase the chances of predicting rejection. Our study presents several deep-learning approaches for quantitative analysis of histological scans for acquiring supportive information. The proposed segmentation methods focus on inflammation, endocardium, and blood vessels. The study compares the experimental results of multiple methods evaluated using real data from medical experts. This study lays the groundwork for future research and demonstrates the potential of deep learning applied to the prediction of transplant rejection.

Název v anglickém jazyce

DeepLabV3+Ensemble for Diagnosis of Cardiac Transplant Rejection

Popis výsledku anglicky

Heart transplantation is a complex procedure, often joined with complications such as cardiac transplant rejection. Current diagnostic methods include regular invasive and time-consuming biopsies followed by histopathological analysis. Deep learning has the potential to significantly enhance speed and objectivity and introduce new information from the obtained sample to increase the chances of predicting rejection. Our study presents several deep-learning approaches for quantitative analysis of histological scans for acquiring supportive information. The proposed segmentation methods focus on inflammation, endocardium, and blood vessels. The study compares the experimental results of multiple methods evaluated using real data from medical experts. This study lays the groundwork for future research and demonstrates the potential of deep learning applied to the prediction of transplant rejection.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30201 - Cardiac and Cardiovascular systems

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Vision Systems : 14th International Conference, ICVS 2023, Vienna, Austria, September 27–29, 2023, Proceedings

ISBN

978-3-031-44136-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

112-122

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Vienna, AT

Datum konání akce

27. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001160756900010